An Arctic sea ice thickness variability revealed from satellite altimetric measurements

A modified algorithm taking into account the first year（FY） and multiyear（MY） ice densities is used to derive a sea ice thickness from freeboard measurements acquired by satellite altimetry ICESat（2003–2008）. Estimates agree with various independent in situ measurements within 0.21 m. Both the fall and winter campaigns see a dramatic extent retreat of thicker MY ice that survives at least one summer melting season. There were strong seasonal and interannual variabilities with regard to the mean thickness. Seasonal increases of 0.53 m for FY the ice and 0.29 m for the MY ice between the autumn and the winter ICESat campaigns, roughly 4–5 month separation, were found. Interannually, the significant MY ice thickness declines over the consecutive four ICESat winter campaigns（2005–2008） leads to a pronounced thickness drop of 0.8 m in MY sea ice zones. No clear trend was identified from the averaged thickness of thinner, FY ice that emerges in autumn and winter and melts in summer. Uncertainty estimates for our calculated thickness, caused by the standard deviations of multiple input parameters including freeboard, ice density, snow density, snow depth, show large errors more than 0.5 m in thicker MY ice zones and relatively small standard deviations under 0.5 m elsewhere. Moreover, a sensitivity analysis is implemented to determine the separate impact on the thickness estimate in the dependence of an individual input variable as mentioned above. The results show systematic bias of the estimated ice thickness appears to be mainly caused by the variations of freeboard as well as the ice density whereas the snow density and depth brings about relatively insignificant errors.

Arctic multiyear sea ice variability observed from satellites:a review

In comparison with seasonal sea ice(first-year ice,FY ice),multiyear(MY)sea ice is thicker and has more opportunity to survive through the summer melting seasons.Therefore,the variability of wintertime MY ice plays a vital role in modulating the variations in the Arctic sea ice minimum extent during the following summer.As a response,the ice-ocean-atmosphere interactions may be significantly affected by the variations in the MY ice cover.Satellite observations are characterized by their capability to capture the spatiotemporal changes of Arctic sea ice.During the recent decades,many active and passive sensors onboard a variety of satellites(QuikSCAT,ASCAT,SSMIS,ICESat,CryoSat-2,etc.)have been used to monitor the dramatic loss of Arctic MY ice.The main objective of this study is to outline the advances and remaining challenges in monitoring the MY ice changes through the utilization of multiple satellite observations.We summarize the primary satellite data sources that are used to identify MY ice.The methodology to classify MY ice and derive MY ice concentration is reviewed.The interannual variability and trends in the MY ice time series in terms of coverage,thickness,volume,and age composition are evaluated.The potential causes associated with the observed Arctic MY ice loss are outlined,which are primarily related to the export and melting mechanisms.In addition,the causes to the MY ice depletion from the perspective of the oceanic water inflow from Pacific and Atlantic Oceans and the water vapor intrusion,as well as the roles of synoptic weather,are analyzed.The remaining challenges and possible upcoming research subjects in detecting the rapidly changing Arctic MY ice using the combined application of multisource remote sensing techniques are discussed.Moreover,some suggestions for the future application of satellite observations on the investigations of MY ice cover changes are proposed.

Arctic sea ice volume export through the Fram Strait from combined satellite and model data:1979–2012

By combing satellite-derived ice motion and concentration with ice thickness fields from a popular model PIOMAS we obtain the estimates of ice volume flux passing the Fram Strait over the 1979–2012 period. Since current satellite and field observations for sea ice thickness are limited in time and space, the use of PIOMAS is expected to fill the gap by providing temporally continued ice thickness fields. Calculated monthly volume flux exhibits a prominent annual cycle with the peak record in March（roughly 145 km3/month） and the trough in August（10 km^3/month）. Annual ice volume flux（1 132 km^3） is primarily attributable to winter（October through May） outflow（approximately 92%）. Uncertainty in annual ice volume export is estimated to be 55 km^3（or 5.7%）. Our results also verified the extremely large volume flux appearing between late 1980 s and mid-1990 s. Nevertheless, no clear trend was found in our volume flux results. Ice motion is the primary factor in the determination of behavior of volume flux. Ice thickness presented a general decline trend may partly enhance or weaken the volume flux trend. Ice concentration exerted the least influences on modulating trends and variability in volume flux. Moreover, the linkage between winter ice volume flux and three established Arctic atmospheric schemes were examined. Compared to NAO, the DA and EOF3 mechanism explains a larger part of variations of ice volume flux across the strait.

Arctic summertime anticyclonic circulation mode and its influence on substantial sea ice depletion:a review

The summertime anticyclonic circulation mode(SACM)is related to recent substantial loss of sea ice in the Arctic.This review outlines the potential causes of the SACM and considers its influence on sea ice depletion.Local triggers(i.e.,sea ice loss and sea surface temperature(SST)variation)and spatiotemporal teleconnections(i.e.,extratropical cyclone intrusion,tropical and mid-latitude SST anomalies,and winter atmospheric circulation preconditions)are discussed.The influence of the SACM on the dramatic loss of sea ice is emphasized through inspection of relevant dynamic(i.e.,Ekman drift and export)and thermodynamic(i.e.,moisture content,cloudiness,and associated changes in radiation)mechanisms.Moreover,the motivation for investigation of the underlying physical mechanisms of the SACM in response to the recent substantial sea ice depletionis also clarified through an attempt to better understand the shifting ice-atmosphere interaction in the Arctic during summer.Therecord low extent of sea ice in September 2012 could be reset in the near future if the SACM-like scenario continues to exist during summer in the Arctic troposphere.

基于卫星观测的2003-2013年北极海冰体积变化估算

北极海冰正处于快速减退时期,北极海冰体积变化是全球气候变化的重要指示因子。本文利用两种卫星高度计数据(ICESat和CryoSat-2)反演得到的海冰厚度数据,结合星载辐射计提取的海冰密集度数据以及海冰年龄数据,估算了近期的北极海冰体积以及一年冰和多年冰体积变化。CryoSat-2观测时段(2011-2013年)与ICESat观测时段(2003-2008年)相比,北极海冰体积在秋季(10-11月)和冬季(2-3月)分别减少了1 426 km^3和412 km^3。其中,秋季和冬季的一年冰的体积增加了702 km^3和2 975 km^3。相反,多年冰分别减少了2 108 km^3和3 206 km^3。多年冰的大量流失是造成北极海冰净储量下降的主要原因。

基于大数据分析系统的冷轧工序质量管控平台功能及应用

鞍钢冷轧工序质量管控平台基于鞍钢全流程产品质量大数据分析系统建立。通过对冷轧过程中的工艺数据、物料信息及状态参数进行采集、存储、整合、处理,并在此基础上,构建冷轧过程监控、质量分析、质量追溯、计划评价及质量预警等功能模块,实现了冷轧过程实时监控及物料异常诊断追溯功能,保证冷轧工序受控,及时发现生产过程异常及其形成原因,保证了生产过程顺行及产品质量稳定性。显著提升了冷轧工序生产效率,提高工艺人员工作效率,明显提高企业竞争力。

SAS聚类算法在外商投资企业经营分析中的应用研究

本文运用大数据分析软件SAS开展了数据挖掘的探索研究工作,挖掘了对人民银行新疆辖区外商投资企业存量权益数据,利用SAS聚类算法对外商投资企业经营状况按照主要指标进行了特征分析,总结出全辖外商投资企业的特征,并针对分类结果提出了相关的监管建议。

Satellite-observed trends in the Arctic sea ice concentration for the period 1979–2016

Arctic sea ice cover has decreased dramatically over the last three decades. This study quanti?es the sea ice concentration(SIC) trends in the Arctic Ocean over the period of 1979–2016 and analyzes their spatial and temporal variations. During each month the SIC trends are negative over the Arctic Ocean, wherein the largest(smallest) rate of decline found in September(March) is-0.48%/a(-0.10%/a).The summer(-0.42%/a) and autumn(-0.31%/a) seasons show faster decrease rates than those of winter(-0.12%/a) and spring(-0.20%/a) seasons. Regional variability is large in the annual SIC trend. The largest SIC trends are observed for the Kara(-0.60%/a) and Barents Seas(-0.54%/a), followed by the Chukchi Sea(-0.48%/a), East Siberian Sea(-0.43%/a), Laptev Sea(-0.38%/a), and Beaufort Sea(-0.36%/a). The annual SIC trend for the whole Arctic Ocean is-0.26%/a over the same period. Furthermore, the in?uences and feedbacks between the SIC and three climate indexes and three climatic parameters, including the Arctic Oscillation(AO), North Atlantic Oscillation(NAO), Dipole anomaly(DA), sea surface temperature(SST), surface air temperature(SAT), and surface wind(SW), are investigated. Statistically, sea ice provides memory for the Arctic climate system so that changes in SIC driven by the climate indices(AO, NAO and DA) can be felt during the ensuing seasons. Positive SST trends can cause greater SIC reductions, which is observed in the Greenland and Barents Seas during the autumn and winter. In contrast, the removal of sea ice(i.e., loss of the insulating layer) likely contributes to a colder sea surface(i.e., decreased SST), as is observed in northern Barents Sea. Decreasing SIC trends can lead to an in-phase enhancement of SAT, while SAT variations seem to have a lagged in?uence on SIC trends. SW plays an important role in the modulating SIC trends in two ways: by transporting moist and warm air that melts sea ice in peripheral seas(typically evident inthe Barents Sea) and by exporting sea ice out of the Arctic Ocean via passages into the Greenland and Barents Seas, including the Fram Strait, the passage between Svalbard and Franz Josef Land(S-FJL),and the passage between Franz Josef Land and Severnaya Zemlya(FJL-SZ).

Retrieving the antarctic sea-ice concentration based on AMSR-E 89 GHz data

Sea-ice concentration is a key item in global climate change research. Recent progress in remotely sensed sea-ice concentration product has been stimulated by the use of a new sensor, advanced microwave scan- ning radiometer for EOS （AMSR-E）, which offers a spatial resolution of 6 km×4 km at 89GHz, A new inver- sion algorithm named LASI （linear ASI） usingAMSR-E 89GHz data was proposed and applied in the antarc- tic sea areas. And then comparisons between the LASI ice concentration products and those retrieved by the other two standard algorithms, ASI （arctic radiation and turbulence interaction study sea-ice algorithm） and bootstrap, were made. Both the spatial and temporal variability patterns of ice concentration differ- ences, LASI minus ASI and LASI minus bootstrap, were investigated. Comparative data suggest a high result consistency, especially between LASI and ASI. On the other hand, in order to estimate the LASI ice concen- tration errors introduced by the tie-points uncertainties, a sensitivity analysis was carried out. Additionally an LASI algorithm error estimation based on the field measurements was also completed. The errors suggest that the moderate to high ice concentration areas （〉70%） are less affected （never exceeding 10%） than those in the low ice concentration. LASI and ASI consume 75 and 112 s respectively when processing the same AMSR-E time series thourghout the year 2010. To conclude, by using the LASI algorithm, not only the sea- ice concentration can be retrieved with at least an equal quality as that of the two extensively demonstrated operational algorithms, ASI and bootstrap, but also in a more efficient way than ASI.

Arctic sea ice thickness changes in terms of sea ice age

In this study, changes in Arctic sea ice thickness for each ice age category were examined based on satellite observations and modelled results. Interannual changes obtained from Ice, Cloud, and Land Elevation Satellite（ICESat）-based results show a thickness reduction over perennial sea ice（ice that survives at least one melt season with an age of no less than 2 year） up to approximately 0.5–1.0 m and 0.6–0.8 m（depending on ice age） during the investigated winter and autumn ICESat periods, respectively. Pan-Arctic Ice Ocean Modeling and Assimilation System（PIOMAS）-based results provide a view of a continued thickness reduction over the past four decades. Compared to 1980 s, there is a clear thickness drop of roughly 0.50 m in 2010 s for perennial ice. This overall decrease in sea ice thickness can be in part attributed to the amplified warming climate in north latitudes. Besides, we figure out that strongly anomalous southerly summer surface winds may play an important role in prompting the thickness decline in perennial ice zone through transporting heat deposited in open water（primarily via albedo feedback） in Eurasian sector deep into a broader sea ice regime in central Arctic Ocean. This heat source is responsible for enhanced ice bottom melting, leading to further reduction in ice thickness.

Recent satellite-derived sea ice volume flux through the Fram Strait： 2011–2015

The Fram Strait（FS） is the primary region of sea ice export from the Arctic Ocean and thus plays an important role in regulating the amount of sea ice and fresh water entering the North Atlantic seas. A 5 a（2011–2015） sea ice thickness record retrieved from Cryo Sat-2 observations is used to derive a sea ice volume flux via the FS. Over this period, a mean winter accumulative volume flux（WAVF） based on sea ice drift data derived from passivemicrowave measurements, which are provided by the National Snow and Ice Data Center（NSIDC） and the Institut Francais de Recherche pour d＇Exploitation de la Mer（IFREMER）, amounts to 1 029 km^3（NSIDC） and1 463 km^3（IFREMER）, respectively. For this period, a mean monthly volume flux（area flux） difference between the estimates derived from the NSIDC and IFREMER drift data is –62 km^3 per month（–18×10~6 km^2 per month）.Analysis reveals that this negative bias is mainly attributable to faster IFREMER drift speeds in comparison with slower NSIDC drift data. NSIDC-based sea ice volume flux estimates are compared with the results from the University of Bremen（UB）, and the two products agree relatively well with a mean monthly bias of（5.7±45.9） km^3 per month for the period from January 2011 to August 2013. IFREMER-based volume flux is also in good agreement with previous results of the 1990 s. Compared with P1（1990/1991–1993/1994） and P2（2003/2004–2007/2008）, the WAVF estimates indicate a decline of more than 600 km^3 in P3（2011/2012–2014/2015）. Over the three periods, the variability and the decline in the sea ice volume flux are mainly attributable to sea ice motion changes, and second to sea ice thickness changes, and the least to sea ice concentration variations.

Increasing Winter Conductive Heat Transfer in the Arctic Sea-ice-covered Areas: 1979–2014

Sea ice is a quite sensitive indicator in response to regional and global climate changes. Based on monthly mean PanArctic Ice Ocean Modeling and Assimilation System(PIOMAS) sea ice thickness fields, we computed the conductive heat flux(CHF) in the Arctic Ocean in the four winter months(November–February) for a long period of 36 years(1979–2014). The calculated results for each month manifest the increasing extension of the domain with high CHF values since 1979 till 2014. In 2014, regions of roughly 90% of the central Arctic Ocean have been dominated by the CHF values larger than 18 Wm^(-2)(November–December) and 12 Wm^(-2)(January–February), especially significant in the shelf seas around the Arctic Ocean. Moreover, the population distribution frequency(PDF) patterns of the CHF with time show gradually peak shifting toward increased CHF values. The spatiotemporal patterns in terms of the trends in sea ice thickness and other three geophysical parameters, surface air temperature(SAT), sea ice thickness(SIT), and CHF, are well coupled. This suggests that the thinner sea ice cover preconditions for the more oceanic heat loss into atmosphere(as suggested by increased CHF values), which probably contributes to warmer atmosphere which in turn in the long run will cause thinner ice cover. This represents a positive feedback mechanism of which the overall effects would amplify the Arctic climate changes.

Effects of Suspended Culture of the Seaweed Laminaria japonica Aresch on the Flow Structure and Sedimentation Processes

The culture of suspended kelp, such as Laminaria japonica Aresch, has arisen in nearshore areas for approximately 30 years since the 1980 s. This long-term activity has significant impact on the regional hydrodynamic and sedimentary environments. In this study the impact was investigated, based on synchronized multi-station data from continuous observations made within and around the culture area. In total, three current velocity profiles were identified inside and on the landward side of the culture area. Based on the current velocity profiles we calculated the boundary layer parameters, the fluxes of erosion/deposition, and the rate of sediment transport in different times at each observation site. Comparison between culture and non-culture periods showed that the presence of suspended kelp caused the reduction in the average flow velocity by approximately 49.5%, the bottom friction velocity by 24.8%, the seabed roughness length by 62.7%, and the shear stress and the flux of resuspended sediment by approximately 50%. From analyses in combination with the corresponding vertical variation of the suspended sediment distribution, it is revealed that the lifted sediments by resuspension is mixed with the upper suspended material, which will modify the regional distribution of suspended sediment. These changes in flow structure and sediment movement will accelerate seabed siltation, which corresponds to the changes in seabed erosion/deposition. However, under the influences of the seasonal changes in kelp growth the magnitude of change with the seabed siltation was not obvious inside the culture area, but a fundamental change was apparent around the culture area.

A review of synoptic weather effects on sea ice outflow through Fram Strait: cyclone vs. anticyclone

Sea ice outflow through Fram Strait is a vital component of the sea ice mass balance of the Arctic Ocean.Previous studies have examined the role of large-scale modes of atmospheric circulation variability such as the Arctic Oscillation,North Atlantic Oscillation,and Dipole Anomaly in the movement of sea ice.This review emphasizes the distinct impacts of synoptic weather on sea ice export as well as on other relevant fields(i.e.,sea ice concentration and sea ice drift).We identify deficiencies in previous studies that should be addressed,and we summarize potential research subjects that should be investigated to further our understanding of the relationship between synoptic weather and sea ice export via Fram Strait.For example,the connection between summertime anticyclones and weakened potential vorticity related to the observed extensive spring Eurasian snow and Siberian Ocean sea ice loss is of considerable interest.In-depth exploration of this type of geophysical mechanism will be particularly useful in assessment of the robustness of such linkages inferred through statistical analyses.

通过间歇化霜控制对无霜冰箱制冷系统的优化

通过程序控制将原风冷无霜冰箱在化霜过程中化霜加热管的持续加热调整为间隙加热,从而有效利用化霜加热管余温,以合理控制蒸发器仓温度,达到均匀化除蒸发器积霜的效果,并且减小蒸发器仓内温度过高而导致的安全隐患,同时达到节能降耗的目的。

图像融合技术在海阳核电工程建设区域表层悬沙浓度遥感反演中的应用

本研究分别采用根据高时间分辨率的GOCI(Geostationary Ocean Imager)数据和高空间分辨率的OLI影像反演得到的悬浮泥沙浓度产品,利用STARFM(Spatial and Temporal Adaptive Reflectance Fusion Model)融合算法,生成高时空分辨率的悬浮泥沙浓度产品,融合后的数据兼具OLI影像的高空间分辨率和GOCI影像的高时间分辨率。与实测数据相比, GOCI原始数据反演的相对误差为56%,融合图像的反演相对误差降低至36%。融合影像可用于探讨研究区从高潮到低潮时期表层悬浮泥沙完整变化,便于分析海阳核电工程建设区域的悬浮泥沙分布受潮流变化的影响,进而了解核电工程建设通过影响悬沙分布对周围环境产生的影响。

Estimation of tropical cyclone parameters and wind fields from SAR images

The traditional method of Synthetic Aperture Radar(SAR)wind field retrieval is based on an empirical relation between the near surface winds and the normalized radar backscatter cross section to estimate wind speeds,where this relation is called the geophysical model function(GMF).However,the accuracy rapidly decreases due to the impact of rainfall on the measurement of SAR and the saturation of backscattered intensity under the condition of tropical cyclone.Because of no available instrument synchronously monitoring rain rate on the satellite platform of SAR,we have to derive the precipitation of the SAR observation time from non-simultaneous passive microwave observations of rain in combination with geostationary IR images,and then use the model of rain correction to remove the impact of rain on SAR wind field measurements.For the saturation of radar backscatter cross section in high wind speed conditions,we develop an approach to estimate tropical cyclone parameters and wind fields based on the improved Holland model and the SAR image features of tropical cyclone.To retrieve the low-to-moderate wind speed,the wind direction of tropical cyclone is estimated from the SAR image using wavelet analysis.And then the maximum wind speed and the central pressure of tropical cyclone are calculated by a least square minimization of the difference between the improved Holland model and the low-to-moderate wind speed retrieved from SAR.In addition,wind fields are estimated from the improved Holland model using the above-mentioned parameters of tropical cyclone as input.To evaluate the accuracy of our approach,the SAR images of typhoon Aere,typhoon Khanun,and hurricane Ophelia are used to estimate tropical cyclone parameters and wind fields,which are compared with the best track data and reanalyzed wind fields of the Joint Typhoon Warning Center(JTWC)and the Hurricane Research Division(HRD).The results indicate that the tropical cyclone center,maximum wind speed,and central pressure are generally consistent with the best track data,and wind fields agree well with reanalyzed data from HRD.